Adaptive Ensemble Kalman Filters for Online Bayesian State and Parameter Estimation

• Activity Number: 288 - Advances in Nonlinear and Non-Gaussian Spatio-Temporal Dynamical Models for Environmental and Ecological Processes
• Type: Topic Contributed
• Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Jonathan Stroud* and Matthias Katzfuss and Christopher Wikle
• Companies: Georgetown University and Texas A&M University and University of Missouri
• Keywords: Bayesian Learning ; Sequential Monte Carlo ; Spatio-Temporal Model ; State-Space Model ; Parameter Estimation ; High-Dimensional

Abstract

This paper proposes new methodology for sequential state and parameter estimation within the ensemble Kalman filter. The method is fully Bayesian and propagates the joint posterior density of states and parameters over time. In order to implement the method we consider two representations of the marginal posterior distribution of the parameters: a grid-based approach and a Gaussian approximation. Contrary to existing algorithms, the new method explicitly accounts for parameter uncertainty and provides a formal way to combine information about the parameters from data at different time periods. The method is illustrated and compared to existing approaches using simulated and real data.